High-speed drilling of carbon fiber reinforced polymer (CFRP) laminates often faces challenges which compromise structural integrity. The novelty of this research lies in combining experimental investigation, statistical modeling, global sensitivity analysis, and evolutionary optimization to simultaneously evaluate different output results. Drilling experiments were conducted to investigate the effects of spindle speed, feed rate, tool diameter, and graphene nanoplatelet content on machining responses. The incorporation of 0.25 wt% graphene nanoplatelets improved thermal conductivity and heat dissipation during drilling, which contributed to reducing machining-induced damage. Response surface methodology was employed to develop predictive models, and the developed models showed statistically significant relationships (p < 0.05). Sobol sensitivity analysis was performed to quantify the relative influence of drilling parameters on each response. The results indicated that feed rate was the most influential parameter affecting thrust force (36.1%), peel-up delamination (62.4%), while spindle speed was the most influential parameter on push-down delamination (55.4%). In contrast, drilling temperature was primarily governed by spindle speed, contributing approximately 65.0% to its variation. Multi-Objective Particle Swarm Optimization (MOPSO) was subsequently applied to determine optimal combinations that simultaneously minimize all the results. Experimental validation under optimized conditions demonstrated good agreement with model predictions by 1.3% to 14.8% deviation. The findings demonstrate that graphene nanoplatelet reinforcement combined with data-driven multi-objective optimization significantly improves the machinability of CFRP laminates and reduces drilling-induced damage, providing a practical framework for enhancing hole quality and process reliability in industrial machining of advanced composite structures, particularly in aerospace and high-performance engineering applications.
SoutisC. Carbon fiber reinforced plastics in aircraft construction. Materials Science and Engineering: A2005; 412(1-2): 171–176. https://doi.org/10.1016/j.msea.2005.08.064
2.
Sheikh-AhmadJYTwomeyJ. High-speed drilling of composite materials: A review. J Manuf Sci Eng2007; 129(5): 1015–1023.
3.
DavimJPReisP. Drilling of carbon fiber reinforced plastics (CFRP) manufactured by autoclave: a study on machining parameters. J Mater Process Technol2005; 167(2-3): 173–179.
BaraheniMTabatabaeiSA. Experimental investigation and optimization of drilling parameters in drilling of carbon fiber reinforced plastics using grey relational analysis. J Compos Mater2019; 53(15): 2095–2107.
6.
DemiralMSaracyakupogluTŞahinB, et al.Minimizing delamination in CFRP laminates: Experimental and numerical insights into drilling and punching effects. Polymers2025; 17(22): 3056. https://doi.org/10.3390/polym17223056
PhadnisVAMakhdumFRoyA, et al.Drilling in carbon/epoxy composites: Experimental investigations and finite element analysis. Compos Appl Sci Manuf2013; 47: 41–51. https://doi.org/10.1016/j.compositesa.2012.11.020
9.
BaraheniMTabatabaeiSAAminiS. Multi-objective optimization of drilling parameters in carbon fiber reinforced polymer composites using desirability function analysis. Mater Today Proc2020; 22: 3120–3128.
10.
XuJEl MansoriM. Wear characteristics of polycrystalline diamond tools in high-speed drilling of CFRP composites. Wear2016; 360-361: 105–114.
11.
DoğanMAYapiciAGemiL, et al.Investigation of the stacking sequence and cutting parameters effect on hole morphology in hybrid FML composites. Compos B Eng2025; 300: 112464. https://doi.org/10.1016/j.compositesb.2025.112464
12.
BalajiMVenkata RaoKMohan RaoN, et al.Optimization of drilling parameters for drilling of TI-6Al-4V based on surface roughness, flank wear and drill vibration. Measurement2018; 114: 332–339. https://doi.org/10.1016/j.measurement.2017.09.051
13.
ErturkATVatanseverFYararE, et al.Effects of cutting temperature and process optimization in drilling of GFRP composites. J Compos Mater2021; 55(2): 235–249. https://doi.org/10.1177/0021998320947143
14.
BhatRMohanNSharmaS, et al.Multiple response optimisation of process parameters during drilling of GFRP composite with a solid carbide twist drill. Mater Today Proc2020; 28: 2039–2046. https://doi.org/10.1016/j.matpr.2020.02.384
15.
ElhadiAAmrouneSSlamaniM, et al.Evaluation of thrust force, delamination and hole quality during drilling an alfa-jute/epoxy natural fiber hybrid composite. J Compos Mater2026; 60(4): 303–320. https://doi.org/10.1177/00219983251362295
16.
ArslaneMSlamaniMElhadiA, et al.Advanced optimization of drilling parameters in composite materials: a topsis-based approach for enhanced manufacturing precision. J Nat Fibers2025; 22(1): 2527276. https://doi.org/10.1080/15440478.2025.2527276
17.
TahmasbiVGhoreishiMZolfaghariM. Investigation, sensitivity analysis, and multi-objective optimization of effective parameters on temperature and force in robotic drilling cortical bone. Proc IME H J Eng Med2017; 231(11): 1012–1024. https://doi.org/10.1177/0954411917726098
18.
BaraheniMHoseiniAMNajimiMR. Investigation on carbon fiber-reinforced polymer combined with graphene nanoparticles subjected to drilling operation using response surface methodology and non-dominated sorting genetic algorithm-II. Proc Inst Mech Eng Part E J Process Mech Eng2024: 09544089241230160.
19.
TahmasbiVZeinolabedin-BeygiAElahiSH, et al.Statistical modeling, optimization and sensitivity analysis of dried turning of aluminum bronze alloy. Sādhanā2022; 47(4): 232. https://doi.org/10.1007/s12046-022-01955-7
20.
ElhadiAAmrouneSSlamaniM, et al.Evaluation of drilling by induced delamination of hybrid biocomposites reinforced with natural fibers: a statistical analysis by RSM. J Compos Mater2024; 58(23): 2515–2530. https://doi.org/10.1177/00219983241271035
21.
RamaswamyAPerumalAV. Multi-objective optimization of drilling EDM process parameters of LM13 Al alloy–10ZrB 2–5TiC hybrid composite using RSM. J Braz Soc Mech Sci Eng2020; 42: 1–18. https://doi.org/10.1007/s40430-020-02518-9
22.
KrishnarajVPrabukarthiARamanathanA, et al.Optimization of machining parameters at high speed drilling of carbon fiber reinforced plastic (CFRP) laminates. Compos B Eng2012; 43(4): 1791–1799. https://doi.org/10.1016/j.compositesb.2012.01.007
23.
WangQJiaX. Multi-objective optimization of CFRP drilling parameters with a hybrid method integrating the ANN, NSGA-II and fuzzy C-means. Compos Struct2020; 235: 111803. https://doi.org/10.1016/j.compstruct.2019.111803
24.
AcharyaBRSethiADasAK, et al.Multi-objective optimization in electrochemical micro-drilling of Ti6Al4V using nature-inspired techniques. Mater Manuf Process2023; 38(15): 1942–1954. https://doi.org/10.1080/10426914.2023.2195906
25.
AamirMTolouei-RadMGiasinK, et al.Recent advances in drilling of carbon fiber–reinforced polymers for aerospace applications: a review. Int J Adv Manuf Technol2019; 105(5): 2289–2308. https://doi.org/10.1007/s00170-019-04348-z
26.
YaşarNGünayM. Experimental investigation on novel drilling strategy of CFRP laminates using variable feed rate. J Braz Soc Mech Sci Eng2019; 41(3): 150. https://doi.org/10.1007/s40430-019-1658-2
27.
SorrentinoLTurchettaSBelliniC. A new method to reduce delaminations during drilling of FRP laminates by feed rate control. Compos Struct2018; 186: 154–164. https://doi.org/10.1016/j.compstruct.2017.12.005
28.
KostagiannakopoulouCLoutasTSotiriadisG, et al.Effects of graphene geometrical characteristics to the interlaminar fracture toughness of CFRP laminates. Eng Fract Mech2021; 245: 107584. https://doi.org/10.1016/j.engfracmech.2021.107584
29.
ThakurRKSinghKKRawatP, et al.Evaluation of graphene nanoplatelets addition and machining methods on the hole quality and bearing strength of glass and carbon fibre reinforced epoxy laminates. J Manuf Process2024; 115: 137–155. https://doi.org/10.1016/j.jmapro.2024.02.016
30.
HeydariHCheraghi KazerooniNZolfaghariM, et al.Analytical and experimental study of effective parameters on process temperature during cortical bone drilling. Proc IME H J Eng Med2018; 232(9): 871–883. https://doi.org/10.1177/0954411918796534
DaviesP. Review of standard procedures for delamination resistance testing. Delamination behaviour of composites. Elsevier, 2008, pp. 65–86.
33.
SafariMTahmasbiVHassanpourP. Statistical modeling, optimization and sensitivity analysis of tool’s geometrical parameters on process force in automatic cortical bone drilling process. Int J Eng2021; 34: 528–535.
34.
TahmasbiVSafariMJoudakiJ. Statistical modeling, sobol sensitivity analysis and optimization of single-tip tool geometrical parameters in the cortical bone machining process. Proc IME H J Eng Med2020; 234(1): 28–38. https://doi.org/10.1177/0954411919882862
35.
HarkareVMangrulkarRThoratO, et al. Evolutionary approaches for multi-objective optimization and pareto-optimal solution selection in data analytics. Applied multi-objective optimization. Springer, 2024, pp. 67–94.
36.
HaddagBAtlatiSNouariM, et al.Analysis of the heat transfer at the tool–workpiece interface in machining: determination of heat generation and heat transfer coefficients. Heat Mass Tran2015; 51(10): 1355–1370. https://doi.org/10.1007/s00231-015-1499-1
37.
MishraSKChowdharyR. Heat generated by dental implant drills during osteotomy—A review: heat generated by dental implant drills. J Indian Prosthodont Soc2014; 14: 131–143. https://doi.org/10.1007/s13191-014-0350-6
38.
LinHJianQBaiX, et al.Recent advances in thermal conductivity and thermal applications of graphene and its derivatives nanofluids. Appl Therm Eng2023; 218: 119176. https://doi.org/10.1016/j.applthermaleng.2022.119176
39.
ZhenLISongmeiYJiangMA, et al. Cutting force and specific energy for rotary ultrasonic drilling based on kinematics analysis of vibration effectiveness. Chin J Aeronaut. 2022; 35(1): 376–387.
40.
BaraheniMAminiS. Influence of machining condition and nano-graphene incorporation on drilling load and hole quality in both conventional drilling and ultrasonic-assisted drilling of CFRP. Arabian J Sci Eng2024; 49(11): 14593–14606. https://doi.org/10.1007/s13369-024-08758-4
41.
MahdiAMakhfiSHabakM, et al.Analysis and optimization of machining parameters in drilling woven carbon fiber reinforced polymer CFRP. Mater Today Commun2023; 35: 105885. https://doi.org/10.1016/j.mtcomm.2023.105885
42.
YaşarNGünayM. The influences of varying feed rate on hole quality and force in drilling CFRP composite. Gazi University Journal of Science2017; 30(3): 39–50.
43.
LiYJiaoFZhangZ, et al.Research on entrance delamination characteristics and damage suppression strategy in drilling CFRP/Ti6Al4V stacks. J Manuf Process2022; 76: 518–531. https://doi.org/10.1016/j.jmapro.2022.02.018
44.
BaraheniMSoudmandBAminiS. Delamination assessment and prediction in ultrasonic-assisted drilling of laminated hybrid composites. Proc Inst Mech Eng Part E J Process Mech Eng2024: 09544089241293584. https://doi.org/10.1177/09544089241293584
45.
HungP-y.LauKFoxB, et al.Effect of graphene oxide concentration on the flexural properties of CFRP at low temperature. Carbon2019; 152: 556–564. https://doi.org/10.1016/j.carbon.2019.06.032
46.
JiaZChenCWangF, et al.Analytical study of delamination damage and delamination-free drilling method of CFRP composite. J Mater Process Technol2020; 282: 116665. https://doi.org/10.1016/j.jmatprotec.2020.116665
47.
BaraheniMSoudmandBHAminiS, et al.Stacked generalization ensemble learning strategy for multivariate prediction of delamination and maximum thrust force in composite drilling. J Compos Mater2024; 58(30): 3113–3138. https://doi.org/10.1177/00219983241289494
48.
ShaoZJiangXGengD, et al.The interface temperature and its influence on surface integrity in ultrasonic-assisted drilling of CFRP/Ti stacks. Compos Struct2021; 266: 113803. https://doi.org/10.1016/j.compstruct.2021.113803
HaranePPUnuneDRAhmedR, et al.Multi-objective optimization for electric discharge drilling of waspaloy: a comparative analysis of NSGA-II, MOGA, MOGWO, and MOPSO. Alex Eng J2024; 99: 1–16. https://doi.org/10.1016/j.aej.2024.04.049
51.
GaoYWangYChenG, et al.Optimization design of blade profile parameters of low-speed and high-torque turbodrill based on GA-LSSVM-MOPSO-TOPSIS method. Machines2025; 13(11): 1034. https://doi.org/10.3390/machines13111034
52.
XiLLiLLiL, et al.Parameter optimization of titanium alloy considering energy efficiency and tool wear based on RBFNN-MOPSO algorithm in milling. J Manuf Process2024; 122: 97–111. https://doi.org/10.1016/j.jmapro.2024.05.070
53.
BaraheniMSoudmandBHAminiS, et al. Burr constitution analysis in ultrasonic-assisted drilling of CFRP/nano-graphene via experimental and data-driven methodologies. J Reinforc Plast Compos; 2024: 07316844231225593.
54.
ThakurRKSinghKK. Evaluation of hole quality to explore the influence of graphene nanoplatelets embedded in epoxy/carbon composite during abrasive water jet drilling. J Manuf Process2023; 85: 569–583. https://doi.org/10.1016/j.jmapro.2022.11.054
